Results 81 to 90 of about 40,689 (263)

Continual Learning for Multimodal Data Fusion of a Soft Gripper

open access: yesAdvanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Challenges and Future Directions in Assessing the Quality and Completeness of Advanced Materials Safety Data for Re‐Usability: A Position Paper From the Nanosafety Community

open access: yesAdvanced Sustainable Systems, EarlyView.
Nanosafety data provide a guiding example for establishing best practices in data management, aligning with FAIR principles and quality criteria. This review explores existing quality assessment approaches for reliability, relevance, and completeness, emphasizing the need for harmonization and adaptation to nanomaterials and advanced materials. The aim
Verónica I. Dumit   +43 more
wiley   +1 more source

Hierarchical micro-blog sentiment classification based on feature fusion

open access: yesDianxin kexue, 2016
Sentiment classification is an important issue of opinion mining.It has a high application value to classify sentiment in micro-blogs.As traditional feature selection method has semantic gap,a neural network language model was used to propose a deep ...
Xianying ZHU   +5 more
doaj   +2 more sources

Prototype-Based Explanation for Semantic Gap Reduction With Distributional Embedding

open access: yesIEEE Access
The demand for interpretable models has driven the exploration of explainable approaches grounded in human-friendly case-based reasoning. Among these approaches, prototype-based methods have proven effective in performing case-based reasoning by ...
Hyungjun Joo   +4 more
doaj   +1 more source

Machine‐Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles

open access: yesAdvanced Science, EarlyView.
Using machine learning on a mega‐scale global dataset (n = 1,336,840) reveals a robust personality trait architecture beyond the Big Five. A Big Two model, broadly capturing social engagement and internal mentation, defines a geometric space that links personality to neurocognitive profiles.
Kaixiang Zhuang   +7 more
wiley   +1 more source

Farnesyltransferase Deficiency in Cardiomyocytes Initiates Senescence and Contributes to Cardiac Fibrosis

open access: yesAdvanced Science, EarlyView.
Lipid overload suppresses SREBF2‐mediated FNTB expression, leading to defective Lamin A maturation and nuclear envelope instability. This nuclear catastrophe triggers a pro‐fibrotic senescence program in cardiomyocytes. Notably, restoring nuclear integrity via AAV9‐based gene therapy effectively attenuates cardiac remodeling, identifying the ...
Yuxiao Chen   +16 more
wiley   +1 more source

High‐Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million‐Patient Diabetes Cohort

open access: yesAdvanced Science, EarlyView.
This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño   +5 more
wiley   +1 more source

Feature Fusion-Based Cross-Modal Proxy Hashing Retrieval

open access: yesApplied Sciences
Due to its cost-effective and high-efficiency retrieval advantages, deep hashing has attracted extensive attention in the field of cross-modal retrieval.
Yan Zhao, Huaiying Li
doaj   +1 more source

Unveil Fundamental Graph Properties for Neural Architecture Search

open access: yesAdvanced Science, EarlyView.
This paper proposes NASGraph, a graph‐based framework that represents neural architectures as graphs whose structural properties determine performance. By revealing structure–performance relationships, NASGraph enables efficient neural architecture search with significantly reduced computation.
Zhenhan Huang   +4 more
wiley   +1 more source

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